AI Trainer / Data Annotator (Remote) – DataAnnotation.tech
Evaluated and ranked AI-generated responses for accuracy, safety, and relevance while supporting RLHF-style improvement workflows. Produced structured feedback highlighting issues such as hallucinations, bias, and logical errors to enhance dataset quality and model performance. Designed prompt variations to test reasoning, creativity, and instruction-following behavior across model outputs. • Rated response quality across safety, factuality, and relevance dimensions. • Performed hallucination and bias detection on model outputs. • Identified logical errors and instruction-following failures. • Fed back structured notes for RLHF datasets and model tuning.